Automatski - The Internet of Things - Privacy in IoT

1. The Internet of Things – Automatski Corp.
http://www.automatski.com
E: Aditya@automatski.com , Founder & CEO
M:+91-9986574181
E: Shubhadeep.dev@automatski.com , Director - Sales
M: +91-8884074204

2. 1. Name
2. Geographic categories smaller than
a state
3. Dates (except for the year) that is
related to an individual. This may
include data of birth, death,
admission, discharge, etc.
4. Telephone numbers
5. Fax number
6. E-mail address
7. Social security number (SSN)
8. Medical record number
9. Health plan beneficiary number
10. Account numbers
11. Certificate or license numbers
12. Vehicle identifiers (e.g. serial
numbers, license plates)
13. Device identifiers
14. Web URLs
15. IP addresses
16. Biometric identifiers (e.g. finger
print, voice print)
17. Full-face photos
18. Any other unique identifying
number, code or characteristic

3.  Is NOT Direct Identification
 Because its relatively easy to De-Identify records
 But rather Re-Identification! Aka Identity Reconstruction! Or Trail
Reconstruction!

4.  Latanya Sweeney’s 2006 research in which 87% of people in the United States can
be identified by combining their ZIP code, birth date and sex.
 Sweeney’s research also found that other types of information can also re-identify
people.
 For instance, 53% of US citizens can be identified by their city, birth date and sex
 While 18% of citizens can be identified by their county, birth date and sex.

5. 1. Access control: This is the traditional model for safeguarding individuals’
privacy. It is also referred to as query restriction, which associates certain data
to a given request in a multi-level relational database.
2. Statistical disclosure control: This method includes a wide variety of techniques,
including suppression, noise addition, perturbing records of a collection.
Statistical disclosure control prevents the receiver of the data from inferring
identities of the individuals.
3. Computational disclosure control: This model prevents the formation of direct
connections from unidentified data to identifiable data. With computational
disclosure control, records appear identical through generalization and
suppression of attributes.
4. Algorithms: This model has been promoted most by the data mining industry to
preserve the privacy of individuals.

6.  Data De-Identification
 Data Minimization
 Degrees of Identification
 De-Identification & Re-Identificiation

7.  Anonymization
 Blurring
 Disclosure Avoidance
 Disclosure Limitation
 Masking
 Perturbation
 Record Code
 Redaction
 Suppression

8.  We are NOT hiding from THE SYSTEM(S)
 We are Hiding from the Unwanted Stalkers and Criminals who have access or can
gain access to The System(s)
 We are Hiding Digital Identity from the Real World Identity
 We are Hiding Digital “Activities” from the Real World Identity

9.  "All problems in computer science can be solved by another level of indirection"

10.  We are NOT hiding from THE SYSTEM(S)
 We are Hiding from the Unwanted Stalkers and Criminals who have access or can
gain access to The System(s)
 We are Hiding Digital Identity from the Real World Identity
 We are Hiding Digital “Activities” from the Real World Identity
Problem of Access &
Disclosure
Problem of In-direction
Problem of In-direction

12.  10-20+ years of Software Engineering experience each
 Global Agile & Technology Consulting, Advisory & Delivery experience of 10-15+ years since Agile and
Tech was in Infancy.
 The first computers we worked on were Atari and ZX Spectrum ;-) And yes after Basic we went to
C/C++ and then straight to Assembly Programming and then -> we began our journey as technologists
 Globally Distributed Global & Fortune Company work Experience
 Worked with companies like BCG, McKinsey, Fidelity, Tesco, Goldman Sachs…
 Long 3-5+ year projects & Over 200+ people globally distributed teams
 Led Double Digit Multi-Billion US$ Projects
 Blended methodology used comprising of Scrum, XP, Lean and Kanban
 From there we rode every wave J2EE, RUP, Six Sigma, CMMI, SIP, Mobile, Cloud, Big Data, Data
Science etc…
 Individually worked with over 300+ Technologies at a time, literally nothing that scares us 
 Authors, Speakers, Coach’s, Mentors, Scientists, Engineers, Technologists, Marketing, Sales, HR,
Finance…
 We are Generalists and we Always start with First Principles.

13.  Please refer to http://automatski.com for more information
 Please go through the 2 minute demo, 5 minute demo…
 And the showcase section of the website for more information…
 Or email us on aditya@automatski.com
 Or just give us a shout on Linkedin, Facebook, Twitter, Email etc.